AWS for Software Companies Podcast

Federico Torreti and Tom Sly of AWS discuss how application developers can leverage Generative AI to enhance cross-app experiences, minimize custom integrations and increase the value-add of enhanced communication features.

Topics Include:

• Federico Torreti intro: What is AWS’ vision for applications
• Focus of Machine Learning & AI
• Ability to build rapid prototypes, experimentation
• Cambrian explosion of SaaS applications
• The challenge of toggling of multiple applications
• Bringing the world of disconnected apps
• Data and focusing on the primary value for customer
• Generating more content will raise number of alerts and potential of burnout
• Tom Sly intro: Being delighted by businesses
• Customers expect personalization from businesses
• More than 150,000 customers leverage AWS for communications strategy
• Leveraging messaging to increase customer delight
• 2 examples: restaurant and medical need
• Session wrap up

Participants:

• Federico Torreti – Head of Product – Amazon Q, Amazon Web Services
• Tom Sly - Director, Special Projects, Amazon Web Services

Dr. Yanbing Li, Chief Product Officer at Datadog, outlines how the company has integrated AI and automation into its incident response framework, helping customers manage both traditional security challenges and emerging AI-specific risks.

Topics Include:

• Introduced talk about incident response and CISO liability
• Datadog founded 14 years ago for cloud-based development
• Platform unifies observability and security for cloud applications
• Current environment has too many fragmented security products
• SEC requires material incident reporting within four days
• Datadog's incident response automates Slack room creation
• Response team includes Legal, Security, Engineering, and Product
• System tracks non-material incidents to identify concerning patterns
• Real-time telemetry data drives incident management automation
• On-call capabilities manage escalation workflows
• Datadog uses own products internally for incident response
• Company focuses on reducing time to incident detection
• AI brings new risks: hallucination, data leaks, design exploitation
• Bits.ai launched as LLM-based incident management co-pilot
• Tool synthesizes events and generates incident summaries
• Bits.ai suggests code remediation and creates synthetic tests
• Security built into AI products from initial design
• Prompt injection prevented through structured validation approach
• Sensitive data anonymized before LLM processing
• Engineering and security teams collaborate closely on AI
• LLM observability becoming critical for production deployments
• Customers need monitoring for hallucinations and token usage
• Datadog extends infrastructure monitoring into security naturally
• Company maintains strong partnership with AWS
• Q&A covered Bits.ai proactive capabilities and enterprise differentiation

Participants:

Yanbing Li – Chief Product Officer - Datadog

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David Gildea of Druva shares their approach to building cost-effective, fast generative AI applications, focusing on cybersecurity, data protection, and the innovative use of LLMs for simplified, natural language threat detection.

Topics Include:

• Introduction by Dave Gildea, VP of Product at Druva.
• Focus on building generative AI applications.
• Emphasis on cost and speed optimization.
• Mention of Amazon's Matt Wood keynote.
• AI experience with kids using "Party Rock."
• Prediction: GenAI as future workplace standard.
• Overview of Druva's data security platform.
• Three key Druva components: protection, response, and compliance.
• Druva's autonomous, rapid, and guaranteed recovery.
• Benefits of Druva’s 100% SaaS platform.
• Handling 7 billion backups annually.
• Managing 450 petabytes across 20 global regions.
• Druva’s high NPS score of 89.
• Introduction to Dru Investigate AI platform.
• Generative AI for cybersecurity and threat analysis.
• Support for backup and security admins.
• Simplified cybersecurity threat detection.
• AI-based natural language query interpretation.
• Historical analogy with Charles Babbage’s steam engine.
• "Fail upwards" model for LLM optimization.
• Using small models first, escalating to larger ones.
• API security and customer data protection.
• Amazon Bedrock and security guardrails.
• Testing LLMs with Amazon’s new prompt evaluation tool.
• Speculation on $100 billion future model costs.
• Session wrap up

Participants:

· David Gildea - VP Product Generative AI, GM of CloudRanger, Druva

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Executives from DataRobot, LaunchDarkly and ServiceNow share strategies, actions and recommendations to achieve profitable growth in today's competitive SaaS landscape.

Topics Include:

• Introduction of panelists from DataRobot, LaunchDarkly & ServiceNow
• ServiceNow's journey from service management to workflow orchestration platform.
• DataRobot's evolution as comprehensive AI platform before AI boom.
• LaunchDarkly's focus on helping teams decouple release from deploy.
• Rule of 40: balancing revenue growth and profit margin.
• ServiceNow exceeding standards with Rule of 50-60 approach.
• Vertical markets expansion as key strategy for sustainable growth.
• AWS Marketplace enabling largest-ever deal for ServiceNow.
• R&D investment effectiveness through experimentation and feature management.
• Developer efficiency as driver of profitable SaaS growth.
• Competition through data-driven decisions rather than guesswork.
• Speed and iteration frequency determining competitive advantage in SaaS.
• Balancing innovation with early customer adoption for AI products.
• Product managers should adopt revenue goals and variable compensation.
• Product-led growth versus sales-led motion: strategies and frictions.
• Sales-led growth optimized for enterprise; PLG for practitioners.
• Marketplace-led growth as complementary go-to-market strategy.
• Customer acquisition cost (CAC) as primary driver of margin erosion.
• Pricing and packaging philosophy: platform versus consumption models.
• Value realization must precede pricing and packaging discussions.
• Good-better-best pricing model used by LaunchDarkly.
• Security as foundation of trust in software delivery.
• LaunchDarkly's Guardian Edition for high-risk software release scenarios.
• Security for regulated industries through public cloud partnerships.
• GenAI security: benchmarks, tests, and governance to prevent issues.
• M&A strategy: ServiceNow's 33 acquisitions for features, not revenue.
• Replatforming acquisitions into core architecture for consistent experience.
• Balancing technology integration with people aspects during acquisitions.
• Trends in buying groups: AI budgets and tool consolidation.
• Implementing revenue goals in product teams for new initiatives.

Participants:

• Prajakta Damle – Head of Product / SVP of Product, DataRobot
• Claire Vo – Chief Product & Technology Officer, LaunchDarkly
• Anshuman Didwania – VP/GM, Hyperscalers Business Group, ServiceNow
• Akshay Patel – Global SaaS Strategist, Amazon Web Services

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

In this AWS panel discussion, Naveen Rao, VP of AI of Databricks and Vijay Karunamurthy, Field CTO of Scale AI share practical insights on implementing generative AI in enterprises, leveraging private data effectively, and building reliable production systems.

Topics Include:

• Sherry Marcus introduces panel discussion on generative AI adoption
• Scale AI helps make AI models more reliable
• Databricks focuses on customizing AI with company data
• Companies often stressed about where to start with AI
• Board-level pressure driving many enterprise AI initiatives
• Start by defining specific goals and success metrics
• Build evaluations first before implementing AI solutions
• Avoid rushing into demos without proper planning
• Enterprise data vastly exceeds public training data volume
• Customer support histories valuable for AI training
• Models learning to anticipate customer follow-up questions
• Production concerns: cost, latency, and accuracy trade-offs
• Good telemetry crucial for diagnosing AI application issues
• Speed matters more for prose, accuracy for legal documents
• Cost becomes important once systems begin scaling up
• Organizations struggle with poor quality existing data
• Privacy crucial when leveraging internal business data
• Role-based access control essential for regulated industries
• AI can help locate relevant data across legacy systems
• Models need organizational awareness to find data effectively
• Private data behind firewalls most valuable for AI
• Customization gives competitive advantage over generic models
• Current AI models primarily do flexible data recall
• Next few years: focus on deriving business value
• Future developments in causal inference expected post-5 years
• Complex multi-agent systems becoming more important
• Scale AI developing "humanity's last exam" evaluation metric
• Discussion of responsibility and liability in AI decisions
• Companies must stand behind their AI system outputs
• Existing compliance frameworks can be adapted for AI

Participants:

• Naveen Rao – VP of AI, Databricks
• Vijay Karunamurthy – Field CTO, Scale AI
• Sherry Marcus Ph.D. - Director, Applied Science, AWS

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/